2 Centre for the Built Environment and Health, School of Population Health, The University of Western Australia, Crawley, WA, Australia

3 Currently Population Health Intervention Research Centre, University of Calgary, Alberta, Canada, Formerly Centre for the Built Environment and Health, School of Population Health, The University of Western Australia, Crawley, WA, Australia

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

This secondary analysis investigated the extent and pattern of one-year tracking of
pedometer-determined physical activity in people who relocated within the same metropolitan
area (T1: baseline and T2: post-relocation). Specifically, data were derived from
the RESIDential Environment Project (RESIDE), a natural experiment of people moving
into new housing developments.

Methods

1,175 participants (491 males, age = 42.6 ± 12.7 years, BMI = 27.2 ± 9.9 kg/m2; 684 females, age = 41.2 ± 11.3 years, BMI = 25.4 ± 5.2 kg/m2) wore a Yamax pedometer (SW-200-024) for seven days during the same season at both
time points. Pearson's product-moment and Spearman's rank order correlations were
used to evaluate the extent of tracking of mean steps/day. Age categories were set
as youngest-29.9 (19 was the youngest in males, 20 in females), 30–39.9, 40–49.9,
50–59.9, and 60-oldest (78 was the oldest in males, 71 in females). Change in steps/day
was also described categorically as: 1) stably inactive < 7,500 steps/day; 2) decreased
activity (moved from ≥ 7,500 to < 7,500 steps/day between T1 and T2); 3) increased
activity (moved from < 7,500 to ≥ 7,500 steps/day between T1 and T2); and, 4) stably
active ≥ 7,500 steps/day at both time points. Stratified analyses were used to illuminate
patterns by sex, age, and BMI-defined weight categories.

Results

Overall, there was a small (non-significant) decrease in steps/day between T1 and
T2 (mean ± SD is -81 ± 3,090 with 95%CI -259 to 97). With few exceptions (i.e., older
women), both Pearson's and Spearman's correlations were moderate (r = 0.30–0.59) to
moderately high (r = 0.60–0.70). The relative change/stability in steps/day (cut at
7,500 steps/day) was not significant across age groups in males (χ2 = 17.35, p = .137) but was in females (χ2 = 50.00, p < .0001). In both males and females the differences across BMI categories
was significant (χ2 = 22.28, p = .001 and χ2 = 15.70, p = .015, respectively). For both sexes, those in the obese category were
more stably inactive (and less stably active) between assessment points compared with
those who were categorized as normal weight.

Conclusion

Despite relocation, Western Australian adults held their rank position to a moderate
to moderately high extent over one year. Categorized and expressed as relative stability/change
over time, sex, age, and BMI patterns were evident.

Introduction

Tracking, with regards to physical activity behaviors, refers to the extent to which
an individual maintains relative position or rank over time [1]. Patterns of tracking include those categorized by sex, age, and indicators of body
mass index (BMI)-defined weight status and physical activity levels that meet suggested
cut points for health-related benefits. To date, much of the tracking literature has
focused on young populations as they transition through adolescence [2,3] and beyond to adulthood [4,5]. Tracking with objective monitoring instruments has only been conducted in small
samples of very young children (using accelerometers) [6,7] and adolescents (using pedometers) [2]. Limited information is available about tracking within adult populations [8] or across simple life span events such as relocation of primary residence. More than
39 million Americans (or about 14% of the population) changed addresses in 2004–2005
[9]. In Australia, over 3 million people (or almost 18% of the population) moved residences
in 2000–2001 [10].

To our knowledge, no previous attempt has been made to describe tracking of adult
physical activity behavior (objectively monitored or otherwise) disrupted by relocation.
Therefore, the purpose of this secondary analysis was to investigate the extent and
pattern of tracking of pedometer-determined physical activity between measures taken
one year a part in a sample that moved residences within Perth, Western Australia,
in the interim.

Methods

The RESIDential Environments Project (RESIDE) is a natural experiment to evaluate
the impact of urban design policies intended to encourage more active transport behaviors
through the creation of safe, convenient pedestrian-friendly neighborhoods with access
to shops, transit and parkland. As part of its longitudinal design, people moving
into housing developments in Perth, Western Australia, were invited to participate
in recurrent surveys and physical activity assessment using pedometers. Details about
the selection of housing developments and subsequent recruitment of participants are
provided in another publication [11]. All participants received written information about the study and provided written
consent before providing any personal data. The present manuscript focuses on participants'
baseline (T1) and 12-month follow-up (T2; after having moved to their new home) data
points and presents a novel analysis not previously published. Specifically, participants
were sent a T1 questionnaire and pedometer up to three months before the anticipated
completion of their new home. The T2 questionnaire was sent approximately 12 months
later with the aim that questionnaires and pedometer monitoring be completed in the
same season.

Participants were asked to wear a Yamax-Digiwalker pedometer (SW-200-024) for seven
days. This model has been shown to provide valid and reliable data [12,13]. Participants were asked to attach the pedometer to the waist-band of their clothing
or belt at the front of the hip. Day-end steps were recorded in a log each night before
retiring to bed. Participants were instructed to not reset the pedometer, except at
the start of the first day of wearing the device. Thus a cumulative step-count was
captured. In addition, participants recorded whether the pedometer was worn each day
(i.e., all day, some of the day or not at all) or if the device had been removed for
any reason throughout the day (e.g., for bathing, showering, or swimming).

Pedometer-determined steps for each day (except for day 1) were derived from the cumulative
count by subtracting the previous day's steps from the current day's steps. Data were
then screened for extreme values. Data for any single day indicating < 1,000 steps
were removed and values > 30,000 steps on any single day were truncated (i.e., replaced
with 30,000 steps). Equivalent cut points have been used to identify outliers among
younger individuals [14]. Moreover, these cut points appear reasonable for our data given that the minimum
and maximum daily steps found in a previous population-based study of Western Australian
adults was 982 and 20,221 steps, respectively [15]. Mean steps/day for T1 and T2 were computed from the total weekly steps divided by
the total days (6.5 ± 1.3 days overall) the pedometer was worn.

Other variables extracted from the original data set included sex, age and BMI (based
on self-reported height and weight) at T1. Overall, 1,813 participants completed questionnaires
at T1, and 75% of these participants (n = 1,356) returned completed T2 questionnaires.
Data were reduced to include only those cases with complete pedometer data at both
time points, BMI at T1, and those women who were not pregnant at T2 and reported no
children less than 2 years of age at T2 (these latter two to conservatively exclude
women considered pregnant at either time point). In total, 51 cases were missing some
pedometer data, 48 were missing BMI data, 25 women were pregnant at T2 and 57 women
had children less than 2 years at T2. After reductions, the final analysis data set
used herein comprised of 1,175 cases (491 males, age = 42.6 ± 12.7 years, BMI = 27.2
± 9.9 kg/m2; 684 females, age = 41.2 ± 11.3 years, BMI = 25.4 ± 5.2 kg/m2). There were no significant differences in steps/day or BMI from the original full
data set; however, those in the reduced data set were on average 6.9 years older (p
< 0.000). Age categories were set as youngest-29.9 (19 was the youngest in males,
20 in females), 30–39.9, 40–49.9, 50–59.9, and 60-oldest (78 was the oldest in males,
71 in females).

Change in steps/day was also described categorically as: 1) stably inactive < 7,500
steps/day; 2) decreased activity (moved from ≥ 7,500 to < 7,500 steps/day between
T1 and T2); 3) increased activity (moved from < 7,500 to ≥ 7,500 steps/day between
T1 and T2); and, 4) stably active ≥ 7,500 steps/day at both time points. The dichotomous
cut point 7,500 steps/day was selected since evidence indicates that health benefits
can be realized and that accepted public health recommendations are achievable at
this threshold [16-18]. Stratified analyses were used to illuminate patterns by sex, age, and BMI-defined
weight categories. Chi-square analyses were interpreted for significance. SPSS 15
was used and alpha was set at p < 0.05.

Table 1 also displays Pearson and Spearman correlations computed between T1 and T2 steps/day,
collated by sex and age category. Overall, both Pearson and Spearman correlations
were moderate to moderately high. This suggests that individuals within the groups
held their rank position to a moderate extent between assessments. A singular exception
was observed in females 60+ years of age; the Pearson correlation (0.238; low) was
non-significant, however, the Spearman correlation (0.304; moderate) was statistically
significant.

Table 2 presents sex-and BMI-defined weight category strata of pedometer-determined physical
activity (steps/day) at T1, T2, change (Δ) between T2 and T1, and Pearson and Spearman
correlations. ANOVA (with between-subjects factors of sex and BMI-defined category)
revealed a significant effect of BMI-category on Δ steps/day (F = 3.100, df = 2, p
= .045); no other effects (or interactions) were significant. Again, both Pearson
and Spearman correlations were moderate to moderately high. This suggests that individuals
within lower BMI-defined weight categories (i.e., normal weight) were relatively more
stable in their rank position over time than those in higher BMI-defined weight categories
(i.e., obese).

The relative change/stability in steps/day (cut at 7,500 steps/day) by sex and age
category is presented in Figure 1. In males, the difference in relative change/stability in steps/day across age categories
was not significant (χ2 = 17.35, p = .137), however, in females the difference was significant at (χ2 = 50.00, p < .0001). Specifically, the proportion of females categorized as stably
active was lower in the youngest age group, was higher in the second and third age
groups, and lower again in the older age groups. Differences in the proportion of
females who are stably inactive are a mirror image of this observation.

The relative change/stability in steps/day (cut at 7,500 steps/day) by sex and BMI-defined
weight category is presented in Figure 2. In both males and females the differences across BMI categories was significant
(χ2 = 22.28, p = .001 and χ2 = 15.70, p = .015, respectively). Notably, for both sexes, those in the obese category
were more stably inactive (and less stably active) between assessment points than
those who were categorized as normal weight.

Discussion

These data represent the first known longitudinal pedometer data in adults. Previously,
Raustorp and colleagues [2] reported pedometer tracking data for 97 Swedish adolescents assessed three times
over five years, essentially capturing their development between 12 and 17 years of
age. Pearson's correlations indicated low to moderate tracking in these adolescents,
with patterns of higher tracking in boys than girls (i.e., correlations ranged from
0.55 in boys to 0.22 in girls). Most of the earlier tracking studies (children, adolescents,
and adult) have relied on self-reported behaviors [1]. Spearman correlations for adult men and women were 0.31 and 0.23, respectively,
in a large British cohort assessed on self-reported frequency of leisure physical
activity at 33 and 42 years of age [8]. Spearman correlations ranged from -0.10 to 0.33 for self-reported time in physical
activity assessed 7 years apart in a Canadian adult population [19]. In the present study, we found that pedometer-determined physical activity behavior
tracks to a relatively higher extent over one year in Australian adults disrupted
by relocation, than both pedometer-assessed physical activity in adolescents and self-reports
of physical activity in adults. Specifically, most of the correlations evaluated herein
(both Pearson's and Spearman's) fit within Malina's [1] suggested ranges for moderate to moderately high tracking of physical activity behavior.
The primary exception was in older females. This group was comparatively less stable
in behavior over time, that is, characterized by a less predictable shifting in rank
order between time points. Although this group was relatively smaller in sample size,
it was also somewhat lower in mean steps/day in addition to representing a more vulnerable
age group.

A higher correlation is anticipated with a briefer time span between assessments [1]. Accordingly, the relatively higher correlations observed herein compared to the
more prolonged duration between pedometer-based assessments of the Swedish adolescents
[2] is somewhat anticipated. In contrast, however, Jackson et al. [6] reported correlations of 0.40 (Spearman) to 0.49 (Pearson) (again, generally lower
than those we found) in 3–4 years old children assessed one year apart with accelerometers.
Together, these findings suggest that physical activity behavior tracking may be more
stable from year to year in adulthood, at least until older age groups (this last
especially for women). Developing children and transitioning adolescents are exposed
to continually changing personal, social, and physical environments, so some degree
of instability in behavioral tracking is to be expected. The relative stability of
these adults in their behavior, however, is especially interesting, given that the
entire sample studied relocated between assessments.

The reasons that people relocate often differ as a function of their place in the
lifecycle: younger and middle aged adults relocate for professional and personal opportunities
while older adults often relocate for improved access to amenities and to be closer
to family [20]. The process of relocation in generally considered a stressful life event [21], especially if it requires long distance moves accompanied with inevitable adjustments
to new environments, services, and supports [20]. However, all of the participants in this study were already residents of Perth,
Western Australia prior to relocating again within this community to different neighborhoods
and locales; disruptions associated with long distance moves should therefore be considered
minimal. Regardless the magnitude and scope of this now common life interruption,
however, it is remarkable to note the overall stability of behavior (whether active
or inactive) between time points in these adults. Overall, 25.9% of participants were
stably inactive and 46.4% were stably active. From a health perspective, the higher
proportion of those who are stably active is desirable, of course [2].

Exploring relative stability/change using a simple stratified analysis and Chi-square
testing, we observed significant differential tracking patterns by sex, age, and BMI-defined
weight categories. For example, when steps/day were categorized dichotomously and
then evaluated for stability of such categorization over time, an age-related pattern
was apparent for females, but not for males. However, it is also interesting to scrutinize
the groups that changed their physical activity behavior over just one year. For example,
of the ten specific sex-and-age strata studied, eight demonstrated a greater proportion
of individuals who decreased their behavior compared to those who increased their
behavior. This is an interesting finding worth pursuing in terms of confirmation and
further exploration within other data sets; promoting maintenance of higher physical
activity levels may be a separate and important approach to population health as opposed
to focusing primarily on interventions directed at increasing behavior of sedentary
individuals.

Overall, however, a tracking pattern of steps/day (expressed as a continuous variable)
appeared to be most influenced by BMI-defined weight status. Further, relative stability
of behavior (expressed as a categorical variable) appeared to be moderated by BMI-defined
weight status for both sexes. To emphasize, normal weight individuals were more stable
in their behavior (i.e., they maintained their position within the group) over time
in contrast to those classified as obese. Scrutinizing Figure 2 further reveals that the proportion of obese individuals who increased their physical
activity over the previous year was higher than those who decreased their behavior.
Whether this phenomenon is merely regression to the mean (since those with higher
BMI tend to take fewer steps/day) or a result of a wider spread personal decision
to change behavior (perhaps driven by a motivation to influence weight) is a question
worthy of future research.

A number of study limitations must be noted. Although this study of objectively monitored
physical activity represents an improvement over self-report estimates, pedometers
are not designed to detect intensity of movement. We are therefore unable to make
firm conclusions about individual's participation in health-related quantities of
at least moderate intensity physical activity, an outcome of public health interest
[22]. However, using the cut point of 7,500 steps/day is a defensible proxy threshold
value for a healthful level of physical activity [16-18]. Further, pedometers "miss" or underestimate non-ambulatory activities like weight
training and cycling, and because they are not worn during water activities, swimming
is not detected at all [23]. However, such activities account for only a small proportion of physical activity
on a population level and the estimated average underestimation is approximately 300–700
steps/day [23]. Another limitation of this study is that weight and height were self-reported; estimates
of overweight and obesity may therefore be underestimated [24]. Regardless of this potential bias, however, we were still able to observe clear
moderating effects of BMI-defined weight categories on tracking of pedometer-determined
physical activity. As is often the case for longitudinal studies, attrition is a threat
to validity. Although study recidivism is evident, the data herein is reasonably representative
of the originally recruited sample. These data, are of course, limited in their generalizability
to populations most similar to Perth, Western Australia. Using similar pedometer brands,
this sample (≅ 8,600 steps/day) was more similarly active compared to a previous independent
Perth sample (≅ 9,500 steps/day) [15] than at least two U.S. samples: Colorado [25] (≅ 6,800 steps/day) and South Carolina (≅ 5,900 steps/day) [26].

In summary, in terms of pedometer-assessed physical activity, Western Australian adults
held their rank position within groups to a moderate to moderately high extent (with
few exceptions) between assessments separated by one year and disrupted by relocation.
The observed pattern of change in steps/day was not influenced by sex or age, but
was influenced by BMI-defined weight categories. Categorized and expressed as relative
stability/change over time (i.e., stably inactive, decreased activity, increased activity,
stably active), sex, age, and BMI patterns were evident. Of concern was the observation
that individuals more frequently decreased (than increased) their physical activity
over one year. Although not within the scope of this current study, it is possible
to examine the additional demographic, personal, and environmental correlates of those
categorized according to their relative stability or change between assessments points
in future work.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

CTL conceived of the secondary analysis and lead analysis, interpretation and writing.
BGC designed the original study, participated in design, interpretation and writing
of this secondary analysis. MK contributed to the design of the original study and
assisted with analysis, interpretation and writing. GMcC was involved in original
data management and contributed to interpretation and writing. All authors read and
approved the final manuscript.

Acknowledgements

Funding from the Western Australian Health Promotion Foundation (Healthway) is gratefully
acknowledged (Grant No. 11828). BGC is supported by a NHMRC Senior Research Fellowship
(#503712) and GMcC by an Alberta Heritage Foundation for Medical Research Postdoctoral
Research Fellowship.

References

Malina RM: Tracking of physical activity and physical fitness across the lifespan.